Robust Long-Term Object Tracking via Improved Discriminative Model Prediction

• URL: http://arxiv.org/abs/2008.04722v2
• Date: Tue, 25 Aug 2020 15:37:50 GMT
• Title: Robust Long-Term Object Tracking via Improved Discriminative Model Prediction
• Authors: Seokeon Choi, Junhyun Lee, Yunsung Lee, Alexander Hauptmann
• Abstract summary: We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker. The proposed method achieves comparable performance to the state-of-the-art long-term trackers.
• Score: 77.72450371348016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose an improved discriminative model prediction method for robust long-term tracking based on a pre-trained short-term tracker. The baseline pre-trained short-term tracker is SuperDiMP which combines the bounding-box regressor of PrDiMP with the standard DiMP classifier. Our tracker RLT-DiMP improves SuperDiMP in the following three aspects: (1) Uncertainty reduction using random erasing: To make our model robust, we exploit an agreement from multiple images after erasing random small rectangular areas as a certainty. And then, we correct the tracking state of our model accordingly. (2) Random search with spatio-temporal constraints: we propose a robust random search method with a score penalty applied to prevent the problem of sudden detection at a distance. (3) Background augmentation for more discriminative feature learning: We augment various backgrounds that are not included in the search area to train a more robust model in the background clutter. In experiments on the VOT-LT2020 benchmark dataset, the proposed method achieves comparable performance to the state-of-the-art long-term trackers. The source code is available at: https://github.com/bismex/RLT-DIMP.

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